Future of data for retail
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Becoming a data-driven organisation can help create a better shopping experience for your customers and drive growth through better decision making. Learn how to ensure your business is 'data-fit' for the future.
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Future of data for retail
- 1. The future of data for retail Jason Nathan Global Head of Data
- 2. 22 The size and complexity of big data continues to amaze MULTIPLYING SOURCES EXPONENTIAL VOLUME INCREASED COMPLEXITY & VELOCITY Credit Cards Company Systems Internet Browsing / Clickstream Social Networks Mobile & Smart Devices Applications Global Production of Data Zettabytes pa Mobile & Online Browsing Data Exabytes pa +40 % +37 % “internet of things” Complexity Videos Reviews GeolocationImages Daily Real Time Velocity
- 3. 3333 I am going to concentrate on three facets: Privacy and the future of the Data Marketplace Organisational Data Capability and what it enables Building high value Data Assets
- 5. 55 Abilitytocapturesuperiorvalue Capability level LEVEL 1 LEVEL 2 LEVEL 3 LEVEL 4 LEVEL 5 Advanced Data Capturing and utilising data beyond Core management (i.e. Customer, digital and competitor data. Joined Data Data joined using Customer, Product, Store keys to allow for deeper analysis of behaviours and performance. Enriched Data Data joined and enriched (segmentations, scoring) continuously; enrichment used for reporting, analytics and activation. Data Partnerships Enriched data used to drive a self-sustaining commercial relationship ecosystem. Both enables the consumption of new data (i.e. social) and monetisation of existing organisational data. Essential Data Core Sales, Product and Stores data captured and used for reporting. The history of data in retail is reflected in the organisational capability of retailers
- 6. 66 A robust data strategy is the foundation for unlocking the value of customer data dunnhumby uses six dimensions to evaluate a retailer’s fitness for the future of data
- 8. 8888 The connected customer generates more data than ever before
- 9. 9 lifestyle what who where life stage mission services out of store needs online day of week how offline Partnerships enable us to better understand and engage throughout the shopping eco-system remind delight selection save time curate inform flexibility reward loyalty ask comment notice consider need pick paycheckout obtain use share options surprise rewards new products recognize save money reject segment search pick visit time of day in-store interests time of year when behavior plan rewards Multichannel Shop Ecosystem v2.0 occasion connect surprise community discover shop buy reflect appreciate help products
- 10. 10 Long Loop Short LoopAbility to Activate Different data latencies matter for their ability to make decisions for customers
- 11. 1111 example: using clickstream data to understand how customers shop a category online 11 Insights: Over and under performance online Breakdown of purchasing across key site areas Breakdown of browsing across key site areas Benchmarking versus total Groceries purchasing behaviour Actions: Create a priority list of category opportunities Create case for taxonomy change Support development of annual media planning
- 12. 1212 example: optimising search results, based on customers’ real search terms 12 Insights: Excel based tool Product performance within the top category search terms Top search terms by product Importance of search to your products Actions: Optimise product descriptions (Brandbank) Use common misspellings to identify search thesaurus entries Prioritise acquisition of sponsored search terms (where available) and inform SEO Inform brand messaging based on customer language
- 13. 1313 Privacy and the future of the Data Marketplace
- 14. 14 Transparency through simple and clear T’s and C’s Level and type of activity – relevant offers and content , right frequency, appropriate channel Security needs to be the top hygiene factor – rigorously embedded across the organisation Security, transparency and activity are key to customer trust Transparency through clear messaging of usage of their data and the value it delivers Pricing is a particularly sensitive topic for customers so transparency is critical to earning trust
- 15. 15 A growing number of start-ups have identified the opportunity Connect, Learn & Earn: “Unlock the power of your data, the world’s first personal data marketplace”. Connect social networks, banking, fitness, google+ and earn based on flat rate. “Rewarding you for your personal data. Discover what your data says about you and earn every time it is used”. Connect social, mobile, todo and browsers - insights generated and rewards provided. Handshake, facilitates direct conversation between companies and customers to gain insights and negotiate rewards. Connect for Convenience: convenient management of loyalty programmes (120+), including points & rewards, and ability to use to scan – logs in on customer’s behalf “Life management platform” – connect social, comms, memberships, browser to make more convenient and co-ordinated. Get insights e.g. “life timeline”, and in future option to sell data. Input for Security & Control: Not-for-profit “digital passport” to manually capture information, focused on convenience, security and control. +more in beta testing/pilot phase
- 16. © dunnhumby 2015 | Confidential16 For more examples of how data can help provide a better customer experience, download one of our latest retail reports: How personalisation is changing Retail Pricing The Social Economy To learn how you can optimise your data asset, please contact: info@dunnhumby.com Thank You
Editor's Notes
- Today I’m going to talk about “What we see as the world of data in retail” Our role at dunnhumby is to help our retail partners realise the value of their data to improve their decision making and optimise their ability to communicate with and serve their customers in a better way. We help them organise and enrich their data asset to deliver those services.
- The increase in the sheer volume of data being generated is well documented and serves to highlight the importance of using data to drive better decision making. If companies want to become data-driven organisations, they need to decide what data they want to use from their own data streams and what data they want to source from third parties. The maturity of data in retail has increased dramatically over the past few years, with some retailers now changing organisational structure to cope with their data asset. However, many organisations still operate with a similar mindset and organisational structure to how they might have done so 10 years ago. There is growing maturity in how they think of data as an asset for their business, but the talent base and organisational design that goes with that has lagged behind.
- We use this model to give clients a method to evaluate and understand where they are on the data journey. It’s not always a fast and easy road. Many companies are inching their way to becoming data-driven organisations, using data to inform decision making. However obvious the business benefits might seem, there is a choice – you can choose to opt out of the data universe. As an example, one major clothing retailer from the UK high street has decided they don’t want to use data other than for operational efficiency. They have no interest in using data to learn more about their customers – data is just used to run the core operations of their business. Becoming a data-driven organisation requires careful investment, with a projected 7 figure spend needed to commit to progress up the maturity curve. I’ll talk a little now about the levels in this data maturity curve: Essential data – any modern retailer should have this – sales data, staff data, basic supply chain data. Advanced data – Retailers at this level typically capture a whole range of data which has latent value, but are not really using it or aware of how to best combine it. Often it resides in an operational silo. We help to identify the value, stewardship, technical implementation and opportunities combining these datasets can bring about. Joined data & Enriched data – This is where some retailers start to join the data meaningfully. It can be joined around customers, around stores, around time dimensions, around products, but requires some effort to put this data together for a specific end in mind. The principal of a “data lake” is set to replace the Enterprise Data Warehouse as a primary mechanic with which to do that. Great retailers add scores to products and customers for purposes of targeting, identifying propensity to lapse, etc. This is data enrichment. Data Partnerships – Retailers at this level have a clear view on the partnerships they create with providers, such as social media platforms, and organisations that help them understand open data. We help connect them with partners who make up their eco-system of partnerships which help them understand their customers better.
- These 6 principles are applicable to most businesses: Robust data collection – Many companies don’t have the right terms and conditions in place at point of collection, but this needs to be at the heart of the design of your data strategy. Data collection should not be an afterthought in technical or legal terms. Talent and team structure - People who are owners and stewards of data within an organisation, often report into IT and finance. They should be connected to Marketing or whomever owns the relationship with the customer. Data Partnerships – Some companies have good partnerships, but these are often only leveraged for single purpose. Revenue generation – Many companies are sitting on data that has value in and of itself and could be leveraged outside their organisation if the right terms are in place. Data privacy – How clear are you about speaking to your customers about what is happening with their data? Single view of the customer – Is your data organised in such a way that it enables the best opportunities for analysis and actionable insight?
- This model helps retailers organise thinking around the data they are investing in, collecting, managing, and presenting. The starting point for any conversation around data needs to be an understanding of what at a high level you want to achieve and then creating a clear model for how your customers interact with you on their shopping trip. Key questions to ask: which of those touchpoints generate data that you own or can access through a 3rd party? Can you can enrich any of those touchpoints with additional data or technology which helps customers and generates data?
- Here are some examples of companies who provide third party data which can be used to enhance your existing data asset.
- There are data where it’s valid and useful to have some time after the event, for modelling. But some data is really useful to have at a particular moment of time (e.g. geo-location data for reaching people at certain locations within store). Only focus on short loop data where you really need to.
- Clickstream data helps to understand the navigational path through an ecommerce website. How does the customer move through the website? Which customers always click on the promotional offer on the front page? Which customers use the search function? Which customers use the favourites function? This data is very useful for allowing the retailer to better understand what the customer cares about most and helps deliver the right thing for them.
- What do we mean by privacy and what should companies be doing about it? Security should be a hygiene factor with investment and infrastructure needs in place to manage this. T&Cs should be another key area of focus for the industry – A challenge that many will face: dealing with the issue of data use becoming so complex that it is difficult to explain in simple terms to customers? The dichotomy of privacy: Customers expect fantastic experiences (e.g. Uber), but in order to do that, individuals need to allow sharing of their data for those experiences to manifest. Nervousness around sharing data is often brought about because consumers don’t feel they have transparency of usage. An example of activity: Optimising pricing – consumers are sensitive about the notion of different people getting offered different prices for the same product (e.g. when buying flights online). Increased transparency from companies around how and why would help consumer understanding, but this is likely to remain a constraint for data and analytics for years to come.
- Data portability is something that is inevitable for consumers. Increasingly consumers will want access to the data that is held about them, and legislation could turbo-charge this. Plenty of organisations have now opened that data out through APIs that allow you as an authenticated user to see how your data is being used. Organisations can put this data together to create meaningful value for other organisations and the individual who has empowered them to use and join that data up. The whole principle of moving ownership of data much more directly into the hands of the individuals is going to be a major dynamic of the data marketplace in the next 5-10 years.